Condition Monitoring of Electrical Cables as Installed in Industrial Processes

ABSTRACT

A system and method for verifying the performance and health of wire systems and end devices, including instruments and processes. A computer runs software that collects data from sampled sensors, stores the data, screens the data for outliers, analyzes the data, performs in situ testing, and generates results of the analysis and testing. The system and method verifies not only the steady state performance of instruments, but also the dynamic performance of instruments and the transient behavior of the processes. In one embodiment, the system performs testing of the wiring system connecting the end devices located at the process. In another embodiment, the system also performs analysis of the amplitude probability density and a power spectral density determined from the sensor, or end device, data. In still another embodiment, the system performs a time domain reflectometry (TDR) analysis for a wiring system connecting an end device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Application is a divisional application of U.S. application Ser.No. 11/100,661, filed Apr. 7, 2005, which is a continuation-in-partapplication of U.S. application Ser. No. 10/438,356, filed May 13, 2003,which issued as U.S. Pat. No. 6,915,237 on Jul. 5, 2005, and U.S.application Ser. No. 11/018,292, filed Dec. 21, 2004, which issued asU.S. Pat. No. 6,973,413 on Dec. 6, 2005, both of which claim the benefitof U.S. Provisional Application Ser. No. 60/380,516 filed on May 14,2002.

BACKGROUND OF THE INVENTION

1. Field of Invention

This invention pertains to a system for verifying the performance ofwire systems and end devices (process instruments and other equipment)as well as the process itself. More particularly, this inventionpertains to providing predictive maintenance and management of aging ofplant instruments and processes by testing and analyzing the instrumentsand equipment, including their wiring systems.

2. Description of the Related Art

Process instruments measure process parameters such as temperature,pressure, level, flow, and flux. A process instrument typically consistsof a sensor to measure a process parameter and associated equipment toconvert the output of the sensor to a measurable signal such as avoltage or a current signal.

Accuracy and response time are two characteristics of processinstruments. Accuracy is a measure of how well the value of a processparameter is measured and response time is a measure of how fast theinstrument responds to a change in the process parameter being measured.

To verify the accuracy of a process instrument, it is typicallycalibrated. To verify the response time of a process instrument, it istypically response time tested. The calibration and response timetesting can be performed in a laboratory, but it is desirable to performthe calibration and response time testing while the instrument isinstalled in the plant and as the plant is operating. When an instrumentis tested while installed in a process, the work is referred to as insitu testing. If this can be done while the plant is operating, the workis referred to as on-line testing. In addition to calibration andresponse time testing, there is value in testing the wiring system of aninstrument (i.e., the cables, connectors, and splices).

BRIEF SUMMARY OF THE INVENTION

According to one embodiment of the present invention, an integratedsystem for verifying the performance and health of wire systems and enddevices, including instruments and processes, is provided. The systemcombines on-line and in situ testing and calibration monitoring. In oneembodiment, the system performs analysis of the wiring system connectedto the end devices. In another embodiment, the system also performsanalysis of the amplitude probability density and a power spectraldensity determined from the sensor, or end device, data. In stillanother embodiment, the system performs a time domain reflectometry(TDR) analysis for a wiring system connecting an end device. In thisembodiment, the system outputs the signals for performing the TDR andthe system analyzes the resulting data.

In one embodiment, the system samples the output of existing instrumentsin operating processes in a manner that allows verification of bothcalibration (static behavior) and response time (dynamic behavior) ofinstruments as installed in operating processes, performs measurementsof calibration and response time if on-line tests show significantdegradation, and integration of these testing tools into a program oftesting that includes the necessary technologies and equipment. Thetests described herein are suitable for performing in-situ using themethods described herein. The methods described herein use the combinedresults of on-line calibration verification, in-situ response timemeasurements, and in-situ cable testing to provide a complete assessmentof an instrument health and aging condition. The tests include, but arenot limited to, loop current step response (LCSR); loop resistance,insulation resistance, inductance, and capacitance measurements (LCR);TDR, and insulation resistance (IR). The test methods for wiring systemsand end devices described herein are useful not only for sensors andinstruments, but also for other electrical equipment such as motors,stators, and actuators.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The above-mentioned features of the invention will become more clearlyunderstood from the following detailed description of the invention readtogether with the drawings in which:

FIG. 1 is a block diagram of one embodiment of the integrated system;

FIG. 2 is a flow diagram of the steps for processing the signals fromone sensor;

FIG. 3 is an block diagram of one embodiment of on-line monitoring ofredundant flow signals;

FIG. 4 is diagram showing a noise component of a sensor signal;

FIG. 5 is a block diagram of one embodiment of noise analysis monitoringshowing waveforms at various points;

FIG. 6 is an ideal power spectrum density (PSD) graph;

FIG. 7 is a representative power spectrum density (PSD) graph;

FIG. 8 is graph of a sensor experiencing drift over a period of time;

FIG. 9 is a graph of a time-domain-reflectometry (TDR) trace for asensor and its cable;

FIG. 10 is a flow diagram of one embodiment for analyzing the data;

FIG. 11 is a flow diagram for one embodiment of comparing the sensorvalue to a process value;

FIG. 12 is a block diagram of an embodiment of one sensor loop;

FIG. 13 is a wiring diagram of one embodiment of a RTD connection testedwith a TDR as illustrated in FIG. 9;

FIG. 14 is an APD graph for a typical normal data distribution;

FIG. 15 is an APD graph for a typical skewed data distribution;

FIG. 16 is an APD graph for a typical normal sensor;

FIG. 17 is an APD graph for a typical defective sensor; and

FIG. 18 is a PSD graph showing a typical sensor noise signal with amodel fit.

DETAILED DESCRIPTION OF THE INVENTION

An integrated system for monitoring the performance and health ofinstruments and processes and for providing predictive maintenance andmanagement of aging of plant instruments and processes is disclosed. Oneembodiment of the system 10, as implemented with a computer 110, isillustrated in FIG. 1. The integrated system 10 detects instrumentcalibration drift, response time degradation, vibration signatures ofthe process and its components, cable condition data, existence andextent of blockages in pressure sensing lines and elsewhere in thesystem, fouling of venturi flow elements, and fluid flow rate, amongother instrument and process conditions and problems.

The system 10 integrates an array of technologies into an apparatus andmethod consisting of software, routines, procedures, and hardware thatare used in an industrial process (e.g., a nuclear power plant) toverify instrument calibration and response time, measure vibration ofprocess components, identify process anomalies, and provide a means todetermine when an instrument must be replaced or when the process needscorrective maintenance. Various embodiments of the invention include oneor more of the following technologies: on-line monitoring of instrumentcalibration drift; noise analysis monitoring the response time ofinstruments, identifying blockages in pressure sensing lines,determining fluid flow rate, and detecting process problems by crosscorrelation of existing pairs of signals; loop current step response(LCSR) technique identifying a value for the response time of resistancetemperature devices (RTDs) and thermocouples if it is determined by thenoise analysis technique that the response time is degraded; time domainreflectometry and cable impedance measurements to identify problems incables, connectors, splices, and the end device (these measurementsinclude loop resistance, insulation resistance, inductance, andcapacitance measurements and are collectively referred to as LCRmeasurements); cross calibration techniques to determine whether a groupof temperature sensors have lost their calibration, provide newcalibration tables for outliers, and identify the sensors that must bereplaced; and empirical techniques to identify fouling of venturi flowelements.

FIG. 1 illustrates an embodiment of the integrated system 10. Numerousplant sensors 102 a, 102 b, . . . 102 n each provide a signal to asignal conditioning module 104 a, 104 b, . . . 104 n, to ananalog-to-digital converter (ADC) 106 a, 106 b, . . . 106 n, and into acomputer 110. The computer 110 provides data to a recorder 114 and adisplay/controller 112. The display/controller 112 communicates with thecomputer 110 to confirm and initiate actions by the computer 110. Thecomputer 110 also provides data to a multiplexer (MUX) 122 and acalibration/test signal module 120, which also is connected to the MUX122. The MUX 122 provides a calibration or test signal to a sensor 102a, 102 b, . . . 102 n, as determined by the computer 110, for testingthe loop or the sensor 102 a, 102 b, . . . 102 n.

As illustrated, the integrated system 10 performs on-line monitoring andin situ testing of sensors 102 a, 102 b, . . . 102 n installed in anindustrial plant, for example, a power plant or a manufacturing plant.On-line monitoring involves recording and plotting the steady-stateoutput of sensors, or instruments, during plant operation to identifythe condition of the sensor and the process, including drift. Forredundant instruments, drift is identified by comparing the readings ofthe redundant instruments to distinguish between process drift andinstrument drift. For non-redundant instruments, process empiricalmodeling using neural networks or other techniques and physical modelingare used to estimate the process and use it as a reference for detectinginstrument drift. Process modeling is also used with redundantinstruments to provide added confidence in the results and account forcommon mode, or systemic, drift. This is important because some genericproblems cause redundant instruments to all drift together in onedirection.

The sensors 102 a, 102 b, . . . 102 n, in one embodiment, includetransmitters monitoring various processes. These transmitters include,but are not limited to, pressure transmitters, flow transmitters,temperature transmitters. In another embodiment, the sensors 102 a, 102b, . . . 102 n include instrument loops in which the signal is derivedfrom an instrument monitoring a process variable. In still anotherembodiment, the sensors 102 a, 102 b, . . . 102 n include smart sensorsthat provide a digital signal to the remainder of the loop. In thisembodiment, the computer 110 of the integrated system 10 receives thedigital signal directly from the sensors 102 a, 102 b, . . . 102 nwithout having the signal pass through an ADC 106 a, 106 b, . . . 106 n.

In one embodiment, the integrated system 10 is an adjunct to the normalplant instrumentation system. That is, the integrated system 10 works inconjunction with the normal, installed plant instrumentation to provideon-line calibration and testing capabilities in addition to the normalmonitoring and control functions of the instruments. Toward that end,the connection to plant sensors 102 a, 102 b, . . . 102 n are made bytapping into the loop signals. For example, with a standard 4-20milliampere current loop, a resister is added to the loop and thevoltage across the resistor is used as the input to the signalconditioning module 104 a, 104 b, . . . 104 n. In a nuclear power plant,either the signal conditioning module 104 a, 104 b, . . . 104 n oranother module provides isolation between the safety related sensor andthe integrated system 10.

In another embodiment of the integrated system 10, multiple plantsensors 102 a, 102 b, . . . 102 n are connected to an input multiplexerthat feeds an ADC that inputs a digital signal to the computer 110. Theinput multiplexer is an alternative to the plurality of ADCs 106 a, 106b, . . . 106 n illustrated in FIG. 1. In still another embodiment of theintegrated system 10, the digital signals representing the sensor valuesare obtained from a plant computer, which is monitoring the plantsensors 102 a, 102 b, . . . 102 n for other purposes, such as operationand control of the plant.

FIG. 2 illustrates a flow diagram of the integrated system 10 for asingle sensor 102 a, 102 b, . . . 102 n. The signal from a sensor 102 a,102 b, . . . 102 n is sampled 202 and the sample data is stored 204. Thesampled data is screened with data qualification 206 to determinewhether the data indicates an outlier, or bad data, 208. If an outlieris indicated, corrective action 210 is determined to be necessary. If anoutlier is not indicated, then the data is analyzed 212. The results ofthe analysis will indicate whether testing is needed 214. If testing isindicated, the appropriate test 216 is performed, otherwise, the datacollection process is repeated by continuing to sample the signal 202.In one embodiment, the results of the analysis 212, after determiningthat testing is not needed 214, are generated 218 as plots, bar charts,tables, and/or reports, which are displayed for the operator andrecorded for future reference. In another embodiment, the results of theanalysis 212 are generated 218 before the testing determination 214. Instill another embodiment, the results of the analysis 212 are generated218 at periodic intervals.

Sampling the signal 202 includes sampling the signals from the output ofinstruments in a manner which would allow one to verify both the staticcalibration and dynamic response time of instruments and the transientbehavior of the process itself. Sampling the signal 202 occurs at asampling frequency that is between direct current (dc) up to severalkilohertz. In one embodiment, a single sensor 102 a, 102 b, . . . 102 nhas two signal conditioning modules 104 a, 104 b, . . . 104 n and twoADCs 106 a, 106 b, . . . 106 n providing two digital signals to thecomputer 110. One ADC 106 a, 106 b, . . . 106 n samples the dc componentof the sensor signal, which provides the data for static calibrationanalysis, including drift. The other ADC 106 a, 106 b, . . . 106 nsamples at rates up to several thousand times per second, which providesthe data for dynamic response analysis, including the noise analysis andprocess transient information. In another embodiment, a single ADC 106a, 106 b, . . . 106 n samples at rates up to several thousand times persecond and the computer 110 stores two data streams, one for staticcalibration analysis and another for dynamic response analysis,vibration measurements and detection of other anomalies.

In one embodiment, storing the data 204 includes storing the sample datain random access memory (RAM) in the computer 110. In anotherembodiment, storing the data 204 includes storing the sample data in apermanent data storage device, such as a hard disk, a recordable compactdisk (CD), or other data storage media.

In one embodiment, the data qualification 206 includes screening thedata using data qualification algorithms to remove bad data. In anotherembodiment, the data qualification 206 includes screening the data todetermine whether a sensor value is an outlier 208. If a sensor value isdetermined to be an outlier 208, corrective action 210 is taken orinitiated. In one embodiment, the corrective action 210 includesalarming the condition, which alerts an operator so that correctiveaction can be taken. In another embodiment, corrective action 210includes initiating in situ testing, such as response time testing, orcalibration. For example, if the sensor 102 a, 102 b, . . . 102 n is anRTD, the corrective action 210 includes one or more of the following insitu tests: LCSR, TDR, cable impedance measurements, and crosscalibration. Cross calibration is performed at several temperatures toverify the calibration of RTDs over a wide temperature range and to helpproduce a new resistance versus temperature table for an outlier. In oneembodiment, one or more of the in situ tests are performed by theintegrated system 10. In one embodiment, the tests are performedautomatically based on rules established by the programming. In anotherembodiment, the tests are performed after the condition is alarmed tothe operator and the operator approves the test to be run.

The data qualification 206, in one embodiment, scans and screens eachdata record to remove any extraneous effects, for example, artifactssuch as noise due to interference, noise due to process fluctuations,signal discontinuities due to maintenance activities and plant trips,instrument malfunctions, nonlinearities, and other problems.

If the sensor data is not an outlier, the data is analyzed 212 andanalysis results are produced. The data analysis 212 performed isdependent upon the data that is sampled and how it is sampled. Dataanalysis 212 involves using available data to estimate and track theprocess variable/value being measured. The process value estimate isthen used to identify the deviation of each instrument channel from theprocess value estimate. A variety of averaging and modeling techniquesare available for analysis of on-line monitoring data for instrumentcalibration verification. More reliable results are achieved when threeor more of these techniques are used together to analyze the data andthe results are averaged. The uncertainties of each technique must beevaluated, quantified, and properly incorporated in the acceptancecriteria. The data analysis 212 includes, but is not limited to, staticanalysis, dynamic response analysis, and transient process analysis.Static analysis includes the process analysis illustrated in FIG. 3 andthe drift analysis illustrated in FIG. 8. Dynamic response analysisincludes the noise analysis illustrated in FIGS. 4 to 7 and 18. Inanother embodiment, the dynamic response analysis includes the timedomain reflectometry analysis illustrated in FIGS. 9 and 13. In stillanother embodiment, the dynamic response analysis includes the analysisof amplitude probability density illustrated in FIGS. 14 to 17.

The analysis results are used to determine whether testing is needed214. If so determined, appropriate tests 216 are performed. In oneembodiment, these tests 216 are the same as identified above withrespect to the corrective action 210. If testing 216 is not required,the process repeats by taking another sample 202.

In one embodiment, each of the functions identified in FIG. 2 areperformed by one or more software routines run by the computer 110. Inanother embodiment, one or more of the functions identified in FIG. 2are performed by hardware and the remainder of the functions areperformed by one or more software routines run by the computer 110. Instill another embodiment, the functions are implemented with hardware,with the computer 110 providing routing and control of the entireintegrated system 10.

The computer 110 executes software, or routines, for performing variousfunctions. These routines can be discrete units of code or interrelatedamong themselves. Those skilled in the art will recognize that thevarious functions can be implemented as individual routines, or codesnippets, or in various groupings without departing from the spirit andscope of the present invention. As used herein, software and routinesare synonymous. However, in general, a routine refers to code thatperforms a specified function, whereas software is a more general termthat may include more than one routine or perform more than onefunction.

FIG. 3 illustrates one embodiment of on-line monitoring of redundantflow signals. Those skilled in the art will recognize that the inputsensors can be of other plant variables, such as pressure, temperature,level, radiation flux, among others, without departing from the spiritand scope of the present invention. The illustrated on-line monitoringsystem uses techniques including averaging of redundant signals 302 a,302 b, 302 c (straight and/or weighted averaging 332), empiricalmodeling 324, physical modeling 326, and a calibrated reference sensor310. The raw data 302 a, 302 b, 302 c, 304, 306, 308, 310 is firstscreened by a data qualification algorithm 312, 314, 316 and thenanalyzed 322, 324, 326, 332, 334 to provide an estimate 350 of theprocess parameter being monitored. In the case of the averaginganalysis, the data is first checked for consistency 322 of the signals.The consistency algorithm 322 looks for reasonable agreement betweenredundant signals. The signals that fall too far away from the otherredundant signals 302 a, 302 b, 302 c are excluded from the average orweighted average 342. In other embodiments, one or more of the referencemethods are used with the exclusion of the others. For example, in oneembodiment, if an empirical model 324 has not been developed for theprocess variable being measured, but a physical model 326 has beendeveloped, the process value 342 developed through straight or weightedaveraging 332 and the process value 346 determined by the physical model332 are used.

The diverse signals, which in the illustrated embodiment include level(L) 304, temperature (T) 306, and pressure (P) 308, are processmeasurements that bear some relationship to the process flow 302 a, 302b, 302 c, which is the measured variable. The diverse signals 304, 306,308 are used in an empirical model 324 to calculate the process flow 344based on those variables 304, 306, 308. The diverse signals 304, 306,308 are also used in a physical model 326 to calculate the process flowbased on those variables 304, 306, 308. The flow value (F2) 344 derivedfrom the empirical model 324 and the flow value (F3) 346 derived fromthe physical model 326, along with the straight or weighted average flow(F1) 342 and the reference flow (F4) 348, are checked for consistencyand averaged 334 to produced a best estimate of the process flow (F)350, which is used to calculate deviations 336 of the flow signals 302a, 302 b, 302 c from the best estimate (F) 350. The deviations 336,provide an output of the signals' 302 a, 302 b, 302 c performance,which, in one embodiment, is represented by a graph 338. In anotherembodiment, the output is used to determine whether testing 214 isrequired.

The reference channel 310 is one channel of the group of redundantsensors in which the process signals, such as the flow signals 302 a,302 b, 302 c, are a part. Upon evaluating historical data, biases mayinherently be in the data as compared to the reference values. Thesebiases can be due to normal calibration differences between instruments,different tap locations, etc. To build confidence in and reconfirm thereference for these comparisons, one of the redundant channels 310should be manually calibrated on a rotational basis so that allredundant channels 302 a, 302 b, 302 c, 310 are manually calibratedperiodically. If redundant channels 302 a, 302 b, 302 c are notavailable, then an accurate estimate of the process parameter fromanalytical techniques 324, 326 are used to track the process anddistinguish instrument drift from process drift.

A process parameter cannot usually be simply identified from measurementof another single parameter. For example, in physical modeling 326,complex relationships are often involved to relate one parameter toothers. Furthermore, a fundamental knowledge of the process and materialproperties are often needed to provide reasonable estimates of aparameter using a physical model 326. Typically, empirical models 324use multiple inputs 304, 306, 308 to produce a single output 344 ormultiple outputs. In doing this, empirical equations, neural networks,pattern recognition, and sometimes a combination of these, and other,techniques, including fuzzy logic, for data clustering are used.

The on-line monitoring illustrated in FIG. 3 identifies calibrationproblems at the monitored point, that is, under the normal processoperating conditions. During normal operations, the monitored point isrelatively constant, accordingly, the illustrated embodiment is aone-point calibration check during steady state conditions. When theprocess is started up or shut down, the process variables change and theon-line monitoring verifies the calibration over the range that thevariable changes under the varying process conditions. When data istaken for a wide operating range, extrapolation is used to verifyinstrument performance above and below the operating range.

The data qualification 312, 314, 316, the consistency checking 322, theempirical model 324, the physical model 326, the straight or weightedaveraging 332, the consistency checking and averaging 334, and thedeviations 336, in one embodiment, are implemented with softwareroutines running on at least one computer 110. In another embodiment,the functions are implemented with a combination of hardware andsoftware.

FIGS. 4 through 7 illustrate noise analysis. FIG. 4 shows a waveform ofa sensor signal 402 plotted as the sensor output 408 versus time 410.Over a long period with the process held stable, the sensor signal 402appears as a dc signal, which has a relatively constant signal level,commonly called steady state value or the dc value. However, if aportion of the signal 402 is examined for a short period with a fastsampling rate, a varying signal 404 is seen. That is, there are naturalfluctuations that normally exist on the output of sensors while theprocess is operating.

The varying signal 404 is the noise or alternating current (ac)component of the signal and originates from at least two phenomena.First, the process variable being measured has inherent fluctuations dueto turbulence, random heat transfer, vibration, and other effects.Secondly, there are almost always electrical and other interferences onthe signal. Fortunately, the two phenomenon are often at widelydifferent frequencies and can thus be separated by filtering. The twotypes of noise must be separated because the fluctuations that originatefrom the process are used in performing the noise analysis, which isused for sensor and process diagnostics, response time testing of thesensor, vibration measurement of plant components, among other uses.

FIG. 5 illustrates one embodiment of noise analysis monitoring showingwaveforms at various points along the process. A sensor signal 502 has awave dc component and a noise component. A high-pass filter or bias 504removes the dc component, leaving only the noise component 506. Thenoise component 506 is amplified 508 to produced an amplified signal510, which is passed through a low-pass filter 512 to produce a processnoise signal 514, which does not contain electrical noise. There arevarious methods available for the analysis of the process noise signal514. One option is referred to as the frequency domain analysis, whichcan be implemented with a Fast Fourier Transform (FFT), and another iscalled the time domain analysis. The illustrated embodiment analyses theprocess noise signal 514 with an FFT 516 to produce a power spectraldensity (PSD) plot 518. In another embodiment, the process noise signal514 is analyzed in the time domain, with autoregressive (AR) modelingbeing one example. An AR model is a time series equation to which thenoise data 514 is fit and the model parameters are calculated. Theseparameters are then used to calculate the response time of a sensor orprovide other dynamic analysis.

FIG. 6 illustrates an ideal PSD, which is a variance of a signal in asmall frequency band as a function of frequency plotted versusfrequency. For a simple first order system, the PSD is all that isneeded to provide a sensor response time, which is determined byinverting the break frequency (Fb) 606 of the PSD. The break frequency606 is the intersection of a line 602, which forms the flat portion ofthe curve 608, with a line 604, which follows the slope of the trailingportion. The ideal PSD of FIG. 6 does not show any resonances or otherprocess effects that may affect the response time determination or othersensor or process diagnostics.

FIG. 7 illustrates a representative PSD which shows a resonance andillustrates how an actual PSD might deviate from the ideal curve 608. APSD 708 is determined for a sensor and the PSD amplitude 702 is plottedversus frequency 704. The solid line 706 is a smoothed trace of thecalculated PSD 708, which contains artifacts that deviate from theideal.

Impulse lines are the small tubes which bring the process signal fromthe process to the sensor for pressure, level, and flow sensors.Typically, the length of the impulse lines are 30 to 300 meters,depending on the service in the plant, and there are often isolationvalves, root valves, snubbers, or other components on a typical impulseline. The malfunction in any valve or other component of the impulseline can cause partial or total blockage of the line. In addition,impulse lines can become clogged, or fouled, due to sludge and depositsthat often exist in the process system. The clogging of sensing linescan cause a delay in sensing a change in the process pressure, level, orflow. In some plants, sensing line clogging due to sludge or valveproblems has caused the response time of pressure sensing systems toincrease from 0.1 seconds to 5 seconds. Clogged sensing lines can beidentified while the plant is on-line using the noise analysistechnique. Basically, if the response time of the pressure, level, orflow transmitter is measured with the noise analysis technique (asillustrated in FIG. 7) and compared to a baseline value, the differenceincludes any delay due to the sensing line length and any blockages,voids, and other restrictions.

FIG. 8 illustrates sensor drift by plotting the amplitude 802 of adrifting sensor signal 814 versus time 804. FIG. 8 also illustrates anon-drifting sensor signal 812 over the same period. Sensor drift is thechange in the steady state value over time of the sensor for a constantprocess value. Typically, sensor drift is detected by trending sensorvalues over a period and comparing the measured values to a known orestimated value.

Sensor, or instrument, drift is characterized as either zero shift orspan shift, or a combination of the two. Zero shift drift occurs when asensor output is shifted by an equal amount over the sensor's entirerange. Span shift drift occurs when a sensor output is shifted by anamount that varies over the sensor's range. Process drift occurs whenthe process being measured drifts over time.

To separate sensor drift from process drift or to establish a referencefor detecting drift, a number of techniques are used depending on theprocess and the number of instruments that can be monitoredsimultaneously. For example, if redundant instruments are used tomeasure the same process parameter, then the average reading of theredundant instruments is used as a reference for detecting any drift. Inthis case, the normal output of the redundant instruments are sampledand stored while the plant is operating. The data are then averaged foreach instant of time. This average value is then subtracted from thecorresponding reading of each of the redundant instruments to identifythe deviation of the instruments from the average. In doing so, theaverage reading of the redundant instruments is assumed to closelyrepresent the process. To rule out any systematic (common) drift, one ofthe redundant transmitters is calibrated to provide assurance that therehave been no calibration changes in the transmitter. Systematic drift issaid to occur if all redundant transmitters drift together in onedirection. In this case, the deviation from average would not reveal thesystematic drift.

Another approach for detecting systematic drift is to obtain anindependent estimate of the monitored process and track the estimatealong with the indication of the redundant instruments. This approach isillustrated in FIG. 3, which is an embodiment using redundant flowsignals, although other process variables are monitored in otherembodiments. A number of techniques may be used to estimate the process.These may be grouped into empirical and physical modeling techniques.Each technique provides the value of a process parameter based onmeasurement of other process parameters that have a relationship withthe monitored parameter. For example, in a boiling process, temperatureand pressure are related by a simple model. Thus, if temperature in thisprocess is measured, the corresponding pressure can be determined,tracked, and compared with the measured pressure as a reference toidentify systematic drift. This approach can also be used to provide areference for detecting drift if there is no redundancy or if there is aneed to add to the redundancy. With this approach, the calibration driftof even a single instrument can be tracked and verified on-line.

FIG. 9 illustrates a graph of a time-domain-reflectometry (TDR) tracefor a sensor, or end device, 1310 and its cable 1314, 1324, 1334. FIG.13 illustrates a wiring diagram of one embodiment of a four-wireresistance temperature detector (RTD) 1310 circuit tested with a timedomain reflectometer as illustrated in FIG. 9. The RTD circuit includesa connection terminal block 1302 connected to a second terminal block1304 via cable 1314. The cable 1314 includes four conductors 1312, 1316at each end connected to the respective terminal blocks 1302, 1304. Thesecond terminal block 1304 is connected to an outside terminal block ata wall penetration 1306, which has an inside terminal block that isconnected to a third terminal block 1308 by cable 1334. The cables 1324,1334 each include four conductors 1322, 1326, 1332, 1336 at each endconnected to the respective terminal blocks 1304, 1306, 1308. The thirdterminal block 1308 is connected to the four leads 1342 of the RTD 1310.The TDR trace of FIG. 9 is plotted as a reflection coefficient 902versus distance 904 from the test point 912 made at the terminal block1302. The TDR traces 922, 924 show the locations along a cable 1314,1324, 1334 where the cable impedance changes. The TDR traces 922, 924 ofFIG. 9 graph the test results for a first pair of conductors 922 and asecond pair of conductors 924. The TDR traces 922, 924 show peaks forcable discontinuities for two pairs of conductors for a remote shutdownpanel 914, a wall penetration 916, and the instrument 918, which can bean RTD or other sensor or instrument. The discontinuities includejoining two cables 1314, 1324, 1334 together, such as at a terminalblock 1302, 1304, 1306, 1308, which in FIG. 9 correspond to the shutdownpanel 914, the wall penetration 916, and the instrument 918. The trace922 for one pair has a short as indicated by the drastic drop downwardsat one point 918. The trace 924 for the other pair appears to be a goodcable pair without fault because the trace 924 does not turn drasticallydownward, indicating a short, nor does the trace 924 turn upwards,indicating an open circuit. The TDR traces 922, 924 are used as atroubleshooting tool to identify, locate, or describe problems, andestablish baseline measurements for predictive maintenance and ageingmanagement. There are electrical tests, mechanical tests, and chemicaltests that are used to monitor or determine the condition of cables. Theelectrical tests, such as the TDR, have the advantage of providing thecapability to perform the tests in situ, often with no disturbance tothe plant operation. If an RTD is also tested using the LCSR, noiseanalysis, and/or self-heating methods, the combined data greatlyenhances the diagnostic capability to identify the cause of a signalanomaly from such a circuit. This is especially true if the measurementshave been performed on the circuit in the past and baseline informationis available to identify changes from a reference condition wheneverything was new or normal. The electrical tests are not restricted toTDR measurements. In particular, measurement of resistance (R),capacitance (C), and inductance (L), commonly referred to as LCRtesting, significantly enhances cable diagnostics capability,particularly when combined with TDR measurements

For example, RTD circuits that have shown erratic behavior have beensuccessfully tested by the TDR method to give the maintenance crewproper directions as to the location of the problem. The TDR techniqueis also helpful in troubleshooting motor and transformer windings,pressurizer heater coils, nuclear instrumentation cables, thermocouples,motor operated valve cables, etc. To determine the condition of cableinsulation or jacket material, in addition to TDR, electrical parameterssuch as insulation resistance, dc resistance, ac impedance, and seriescapacitance are measured.

FIG. 10 is a flow diagram of one embodiment of functions performed bythe integrated system 10. The signal data is stored 1002 as a firststep. After storing signal data 1002, the data is analyzed 1004. Theresults of the analysis 1004 are used to determine whether action isrequired 1006 to further test or correct a found condition. In oneembodiment, storing the signal data 1002 is performed by the computer110 through a routine.

The data analysis 1004, in one embodiment, is performed by the computer110 through one or more routines. For example, the on-line monitoringillustrated in FIG. 3 is performed by software run by the computer 110.Also, the noise analysis and drift analysis are performed by softwarerun by the computer 110. One or more of these analysis techniques can beused for each sensor. The data analysis 1004 performed providesinformation on the performance and health of the monitored instrumentsand processes.

The results of the data analysis 1004 are used to determine whetheraction is required 1006. The actions required 1006, in one embodiment,are performed by the computer 110 through one or more routines. Theactions required 1006 include one or more of the LCSR, TDR, cableimpedance measurements, and cross calibration. Additionally, the actionsrequired 1006, in other embodiments, include alarming an out oftolerance condition and awaiting a response by an operator to continuecorrective action. In one embodiment, the corrective action is performedby the integrated system 10. In another embodiment, the correctiveaction is performed by another system after being identified by theintegrated system 10.

FIG. 11 illustrates one embodiment of the data analysis 1004 anddetermination of whether action is required 1006. A sensor value iscompared to a process value 1102 to determine whether there is adeviation 1104 which would require determining an action to take 1106 ifthe deviation 1104 is actionable. If there is not a deviation 1104,there is, in the illustrated embodiment, a delay 1108 in processingbefore making the next comparison 1102, thereby completing the loop. Inanother embodiment, the next comparison 1102 is performed after thedeviation determination 1104 without waiting for a defined delay 1108.The process value used for the comparison can be based on an empiricalmodel, on a physical model, on an average of redundant sensor values, oron other techniques or a combination of techniques for determining theprocess value at the time of the comparison to the measured sensorvalue.

FIG. 12 illustrates a block diagram of an embodiment of one sensor loopshowing a sensor 102 feeding an isolator 1204, which isolates theinstrument loop from the integrated system 10 such that the integratedsystem 10 does not affect the normal operation of the sensor loop. Inone embodiment, the isolator 1204 is a resister in the current loop ofwhich the sensor 102 is a part. The voltage across the resistor is thesignal provided to the high-pass filter/bias offset module 504 and thedata screening module 1208. In another embodiment, the isolator 1204 isa safety related isolation module such as used in a nuclear power plantto isolate safety related components and circuits.

The isolator 1204 provides a signal to a high-pass filter or bias offset504, an amplifier 508 and a low-pass and anti-aliasing filter 512, whichoutputs a signal to an ADC 106 a 1. This ADC 106 a 1 provides a digitalsignal suitable for noise analysis. In one embodiment, the low-passfilter 512 provides filtering to remove the electrical noise on thesignal from the sensor 102. In another embodiment, the low-pass filter512 provides anti-aliasing filtering, which reduces the high frequencycontent of the signal to better enable digital sampling by the ADC 106 a1.

The isolator 1204 also provides a signal to a data screening module1208, which outputs a signal to an ADC 106 a 2. This ADC 106 a 2provides a digital signal suitable for process monitoring and driftanalysis. The two ADCs 106 a 1, 106 a 2 supply digital signals to thecomputer 110.

In another embodiment, the signals from the sensor 102 are obtained viaa data acquisition circuit. In still another embodiment, the sensor 102or the isolator 1204 provides a digital output, in which case the ADCs106 a 1 to 106 a 2 are not necessary and the data screening 1208, thefiltering 504, 512, and amplification 508 are performed within thecomputer 110.

The embodiment illustrated in FIG. 12 uses a combination of hardware andsoftware to form the integrated system 10. In one embodiment, eachsensor 102 a through 102 n has at least one ADC 106 a to 106 n. If theloop requires it, a data screening module 1208 is used to feed the ADC106 a to 106 n. Also, if the loop is such that a noise analysis is to beperformed, the high-pass filter or bias offset 504, the amplifier 508and the low-pass and anti-aliasing filter 512 are used and outputs asignal to another ADC 106 a 1 to 106 n 1. The computer 110 performs theprocessing illustrated in FIG. 2. In one embodiment, the correctiveaction 210 and test 216 functions illustrated in FIG. 2 are performedunder computer 110 control through additional circuits communicatingwith the computer 110 and connected to the sensor 102.

The integrated system 10 is implemented with at least one computer 110.Although not meant to be limiting, the above-described functionality, inone embodiment, is implemented as standalone native code. Generalizing,the above-described functionality is implemented in software executablein a processor, namely, as a set of instructions (program code) in acode module resident in the random access memory of the computer. Untilrequired by the computer, the set of instructions may be stored inanother computer memory, for example, in a hard disk drive, or in aremovable memory such as an optical disk (for eventual use in a CD ROMdrive) or a floppy disk (for eventual use in a floppy disk drive), ordownloaded via the Internet or other computer network.

In addition, although the various methods described are convenientlyimplemented in a general purpose computer selectively activated orreconfigured by software, one of ordinary skill in the art would alsorecognize that such methods may be carried out in hardware, in firmware,or in more specialized apparatus constructed to perform the requiredsteps.

FIG. 14 illustrates an Amplitude Probability Density (APD) graph with atypical normal data distribution. FIG. 15 illustrates an APD graph witha typical skewed data distribution. One example where such analysis isuseful is in determining the aging condition of neutron detectors innuclear power plants where problems can occur due to degradation of thedetector, its cables, connectors, or a combination of them. In such acase, the TDR, insulation resistance (IR), LCR, and other in-situ cablemeasurement techniques combined with noise analysis technique increasesthe diagnostic capability as to the condition of the neutron detectorand its associated wiring system. For example, the noise analysistechnique is used to identify such dynamic performance indicators as theAPD function of the detector noise output, its response time, and otherdynamic characteristics to be considered with the cable testing resultsto improve the capability to determine if the neutron detector systemhas degraded.

With respect to testing of neutron detectors as end devices, in additionto the noise analysis technique, the pulse response test is available.The response time of a neutron detector is related to the mobility ofthe ions and electrons in the gas and the driving potential (appliedvoltage). The response time is approximated as a function of the ion andelectron velocity. Stepping, or pulsing, the high voltage applied to thedetector, and then measuring the resulting ion and electron velocity,allows the response time to be determined. In other embodiments, thistest is performed as a supplement to noise analysis or in lieu of noiseanalysis.

FIGS. 14 and 15 show APD plots of a sensor noise output for two sensors.Amplitude Probability Density is plotted along the y-axis 1414 and thepercent data value is plotted along the x-axis 1412. In FIG. 14, anormal Gaussian distribution curve 1402 is plotted along with the sensornoise output data 1404. As can be seen, the sensor noise output data1404 closely follows the normal distribution curve 1402, therebyindicating that the sensor is operating normally. In FIG. 15, a normaldistribution curve 1502 is plotted along with the sensor noise outputdata 1504 for a sensor deviating from the norm. As can be seen in FIG.15, the sensor noise data 1504 does not follow the normal distributioncurve 1502, but is skewed to one side of the Gaussian peak. A skewnessvalue is calculated and trended for each sensor to identify the on-setof sensor anomalies and to be able to take corrective action beforesensor performance degrades beyond an acceptable point. For a normalsensor (including normal cables), the skewness value should be nearzero. As the sensor output becomes anomalous or its cables becomedefective, the skewness value departs from zero. Skewness is alsoreferred to as the third moment of the output noise data from a sensor;the first moment being the mean value and the second moment being thesignal variance. A fourth moment called kurtosis (or flatness) is alsoused. The kurtosis is a measure of the peakedness of the APD. Thesharper the APD peak, the larger the kurtosis value and vice versa.

FIG. 16 illustrates an APD graph for a typical normal sensor. FIG. 17illustrates an APD graph for a typical defective sensor. AmplitudeProbability Density is plotted along the y-axis 1614 and the data valueis plotted along the x-axis 1612. In FIG. 16, a normal Gaussiandistribution curve 1602 is plotted along with the sensor output data1604. As can be seen, the sensor output data 1604 closely follows thenormal distribution curve 1602, thereby indicating that the sensor isoperating normally. In FIG. 17, a normal distribution curve 1702 isplotted along with the sensor output data 1704 for a sensor deviatingfrom the norm. As can be seen in FIG. 17, the sensor data 1704 does notfollow the normal distribution curve 1702, but deviates drastically fromthe Gaussian curve 1702. The sensor data 1704 illustrated in FIG. 17indicates a defective sensor. Cable defects also cause such departuresfrom a normal distribution 1702. As such, APD plots are used not only todetect sensor problems, but also to identify problems in wiring systems.

FIG. 18 illustrates a power spectral density (PSD) graph showing atypical sensor noise signal with a model fit. A noise signal can beFourier transformed using a Fast Fourier Transform (FFT) algorithm andits PSD calculated. The PSD has information about the dynamic health ofthe sensor. For example, the PSD break frequency and roll off rate canbe measured and tracked to identify changes in detector dynamics. Also,the PSD data can be fit to a detector model to calculate and trackresponse time as a means of determining the on-set of sensor degradationand to separate sensor problems from cable problems.

FIG. 18 plots a normalized PSD 1832 against the y-axis 1804 versusfrequency against the x-axis 1802. The sensor noise data 1832 isplotted, along with a line 1812 showing the steady state value, a decadeline 1814 showing the roll-off rate, and a model fit curve 1822. A breakfrequency line 1806 parallel to the y-axis 1804 shows the intersectionof the steady state line 1812 with the decade roll-off line 1814. Theslope of the decade roll-off line 1814 identifies the roll off rate ofthe sensor. The roll off rate, along with the frequency of the breakfrequency line 1812, are measured and trended for diagnostics of dynamicdegradation of sensors or their constituents.

From the foregoing description, it will be recognized by those skilledin the art that an integrated system 10 for verifying the performanceand health of instruments and processes has been provided. Inparticular, the system 10 provides for diagnosis of electrical andelectronic circuits including the cables, the connectors, and the enddevices, as well as the circuit cards and other electronics that are inthe path of a signal from a sensor, or other end device, to itsindicator. The system 10 monitors plant sensors and analyzes thecondition of the sensors, cables, and processes being monitored. Theanalysis includes a dynamic analysis and an analysis of cable test data.The end devices being monitored provide signals, either during normaloperation or as a result of test signals applied to the devices, thatare analyzed to provide cable or wiring condition information. Thisanalysis of cable condition is performed in conjunction with the dynamicanalysis. In another embodiment, the system 10 takes corrective actionas determined by the analysis results. The corrective action includestesting performed in situ, alarming out of tolerance conditions to anoperator, initiating work orders for investigation by maintenanceworkers, or any other task suitable for the condition of the sensor orprocess.

While the present invention has been illustrated by description ofseveral embodiments and while the illustrative embodiments have beendescribed in considerable detail, it is not the intention of theapplicant to restrict or in any way limit the scope of the appendedclaims to such detail. Additional advantages and modifications willreadily appear to those skilled in the art. The invention in its broaderaspects is therefore not limited to the specific details, representativeapparatus and methods, and illustrative examples shown and described.Accordingly, departures may be made from such details without departingfrom the spirit or scope of applicant's general inventive concept.

1. A method for verifying the performance and health of a plurality ofinstruments and processes, said method comprising: sampling data from aplurality of sensors, said sampled data corresponding to a plurality ofsignals each representing an output from one of a plurality of enddevices as measured at the end of a cable; storing said sampled data;analyzing said sampled data, said step of analyzing including performingat least one analysis selected from a group including an analysis of anamplitude probability density, an analysis of a power spectral density,and an analysis of a time domain reflectometry; and determining whethera corrective action is required by said analysis results; and initiatingsaid corrective action if determined to be required.
 2. The method ofclaim 1 wherein said step of analyzing includes performing at least oneof a static analysis and a dynamic analysis.
 3. The method of claim 1wherein said step of analyzing includes performing a static analysis anda dynamic analysis.
 4. The method of claim 1 further including a step ofscreening said sampled data after said step of sampling data.
 5. Themethod of claim 1 wherein said corrective action includes at least oneof an instrument calibration and an in situ test.